Method and system for automatically generating salary data in express industry

Abstract

The invention discloses an automatic salary data generation method and system for the express industry, and relates to the field of salary data processing, and the method comprises the steps: creating a salary model, naming the salary model, and selecting an applicable person; uploading the historical salary list file of the selected applicable personnel to the system, and obtaining related salary data; analyzing the salary document of the historical months, and obtaining element items of the franchise company; establishing a mapping relation between the franchise company element items and the standard element items through an intelligent matching algorithm; and presenting the generated mapping relation to the financial staff of the franchise company, and adjusting and confirming the matching rule by the financial staff of the franchise company. According to the method, the advanced natural language processing technology is utilized, intelligent analysis of various salary related terms and descriptions is supported, the accuracy and consistency of salary calculation are ensured, automatic salary generation is achieved, a large amount of manpower and time are saved, and better use prospects are brought.

Description

Technical Field

[0001] The present invention relates to the technical field of salary data processing, and in particular to a method and system for automatically generating salary data for the express delivery industry. Background Art

[0002] In the express delivery industry, the efficient operation of express delivery services depends on the coordination and management of multiple links, among which the "elements" cover various assessment standards and deduction items involved in the process from pickup to delivery. These elements play a key role in ensuring service quality, controlling costs and motivating employees. As the implementing entity of express delivery services, franchise companies usually need to design flexible and targeted salary structures based on the responsibilities and performance of different positions. For example, the salary structure of couriers may be significantly different from the salary system of back-office operators and customer service staff.

[0003] However, most franchise companies currently face many challenges in wage calculation. First, the lack of a dedicated wage calculation system means that these companies usually rely on manual data collection and calculation, which is not only time-consuming and labor-intensive, but also prone to errors. Due to the large business volume and high staff turnover in the express delivery industry, the efficiency of manual calculation cannot meet actual needs, which in turn affects employee enthusiasm and the overall efficiency of the company's operations. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for automatically generating salary data for the express delivery industry, so as to solve the problems raised in the above background technology.

[0005] A method for automatically generating salary data for the express delivery industry, the method for automatically generating salary data comprising:

[0006] Create a salary model, name the salary model and select applicable personnel;

[0007] Upload the historical payroll files of the selected applicable personnel into the system to obtain relevant salary data;

[0008] Parse the payroll files of historical months and obtain the elements of the affiliated companies;

[0009] Through intelligent matching algorithms, a mapping relationship between franchised company elements and standard elements is established;

[0010] The generated mapping relationship is presented to the financial personnel of the franchised company, who adjust and confirm the matching rules.

[0011] Preferably, the steps of creating a salary model, naming the salary model and selecting applicable personnel specifically include:

[0012] The user logs in to the salary management system using an account, enters the salary model management module, creates a new salary model, and enters basic information of the salary model, including the model name and description;

[0013] On the page for creating a new model, fill in the model name, select applicable personnel, and select all employees in a specific department. After completing the selection, the user needs to view the list of selected personnel, verify, and save the selected personnel;

[0014] After completing the naming and selecting the applicable personnel, the user clicks the "Save Model" button, and the system automatically saves the salary model and related personnel information to the database;

[0015] After saving, return to the salary model management page and upload the historical payroll file.

[0016] Preferably, uploading the historical payroll file of the selected applicable personnel to the system to obtain relevant salary data specifically includes:

[0017] The user logs in to the salary management system with his / her own account, finds and clicks the "Historical Payroll Upload" module in the main interface to enter the upload interface;

[0018] On the upload page, users need to select the salary model they created previously to ensure that the uploaded file matches the model;

[0019] Select the appropriate payroll model from the drop-down menu, find "Upload File", click the button to open the file selection dialog box, in the file selection dialog box, browse your computer to find and select the historical payroll file containing the selected applicable personnel;

[0020] After selecting the file, the file name is displayed in the corresponding position on the upload page, and the user confirms that the selected file is correct;

[0021] Click the "Upload" button, the system starts uploading the file to the server, the system displays the upload progress bar, and obtains the relevant salary data. After the file is uploaded, the system automatically parses the file and extracts the relevant salary data;

[0022] After the upload is complete, the system will display the parsing results, including successfully extracted data items, error messages, and data integrity checks;

[0023] The user needs to check whether the data is accurate and ensure that there are no omissions or errors. After checking the parsing results, the user clicks the "Confirm Data" button to save the extracted salary data into the system;

[0024] If an error is found, the user selects "Re-upload" and repeats the steps. After confirming the data, the user chooses to return to the upload module.

[0025] Preferably, the parsing of the payroll file of the historical month and obtaining the elements of the affiliated company specifically includes:

[0026] Open the payroll file, check the column headers of the file, identify the columns containing the "franchise company" information, and get the layout and format of the data;

[0027] Check data integrity to ensure there are no missing values ​​and format errors, clean the data, and remove unnecessary rows and columns;

[0028] According to the column headers of the file, find the column containing the information of the franchised company, copy the data of the relevant column, and use the data filtering function to extract the required information;

[0029] Paste the extracted franchise company information into a new table, organize it, remove duplicates, sort and classify the data;

[0030] Count the number of people and total wages of each company, and use pivot tables and chart tools to display the analysis results;

[0031] Save the sorted franchise company data as a new file, write a data analysis report, and summarize the analysis results.

[0032] Preferably, the establishment of a mapping relationship between the franchise company element items and the standard element items by using an intelligent matching algorithm specifically includes:

[0033] Collect data on franchised companies, including company name and address, and collect data on standard elements, including standard name and classification;

[0034] Ensure data format is consistent and remove duplicates and missing items;

[0035] Remove redundant characters and spaces, handle uppercase and lowercase letters, standardize names, and perform word segmentation on text data;

[0036] Extract feature vectors for each element and convert text into numerical vectors using TF-IDF;

[0037] Calculate the similarity between the elements of the franchised company and the standard elements;

[0038] Set a similarity threshold, select matching items above the threshold as valid mappings, record the elements of the franchised company and the corresponding standard elements, use the cross-validation method to verify the results, ensure the accuracy of the matching results, and recalculate inaccurate mapping items;

[0039] Regularly update standard element items and franchised company element item data, re-run the matching algorithm, and ensure the timeliness of the mapping relationship.

[0040] Preferably, the calculating of the similarity between the franchised company element item and the standard element item specifically includes:

[0041] Among them, the cosine similarity calculation formula is:

[0042]

[0043] In the formula, A and B represent two vectors, n represents the dimension of the vector, and A i represents the value of the i-th element in vector A, B i represents the value of the i-th element in vector B, represents the dot product of vectors A and B, represents the square of the modulus of vector A, Represents the square of the magnitude of vector B.

[0044] Preferably, presenting the generated mapping relationship to the financial personnel of the franchised company, and the financial personnel of the franchised company adjusting and confirming the matching rules specifically includes:

[0045] Organize and generate detailed reports containing mapping relationships, clearly presenting data and similarity results;

[0046] Organize a meeting and invite financial personnel and related teams to introduce the background and significance of the mapping relationship, display the mapping relationships one by one, and explain the basis and calculation method of each mapping;

[0047] Seek opinions and suggestions from finance staff, identify inaccurate mapping items that need to be adjusted, and adjust the mapping rules based on the feedback. Finance staff confirm the adjusted mapping relationship and conduct secondary verification to ensure accuracy;

[0048] Document the final confirmed mapping relationship and adjustment rules, set up a follow-up tracking plan, and regularly review the effectiveness and accuracy of the mapping relationship.

[0049] A system for automatically generating salary data for the express delivery industry, the system for automatically generating salary data comprising:

[0050] Personnel management module: used to manage the basic information of all personnel in the company;

[0051] Basic salary management module: The basic salary management module is electrically connected to the personnel management module, and the basic salary management module is used to manage the basic salary information of employees;

[0052] Commission management module: The commission management module is electrically connected to the basic salary management module, and the commission management module is used to define a commission plan linked to employee performance;

[0053] Subsidy management module: The subsidy management module is electrically connected to the commission management module, and the subsidy management module is used to manage various subsidy projects;

[0054] Assessment deduction management module: the assessment deduction management module is electrically connected to the subsidy management module, and the assessment deduction management module is used to record the deductions caused by failure to meet the assessment standards;

[0055] Standard element item management module: The standard element item management module is electrically connected to the assessment deduction management module, and the standard element item management module is used to maintain the information of the standard element items.

[0056] The application layer specifically includes:

[0057] Data synchronization module: realizes data synchronization between various functional modules to ensure real-time sharing and updating of information;

[0058] Data cleaning module: cleans the original data to remove errors, duplications and invalid data;

[0059] Machine learning module: Apply machine learning algorithms to analyze historical salary data and provide intelligent decision support and salary prediction models;

[0060] Rule engine module: flexibly defines and manages various rules for salary calculation, supports dynamic adjustment, and allows financial personnel to handle complex salary calculations without modifying the underlying code, greatly improving work efficiency;

[0061] Payroll generation and refresh module: automatically generates and refreshes payroll according to the set payroll scheme and calculation rules to ensure timely update and accurate generation;

[0062] Log module: comprehensively record system operation logs and track key operations in the payroll processing process.

[0063] The middle layer specifically includes:

[0064] Personnel management: responsible for managing the basic information of all personnel in the company, including personal information, position, department, date of employment, etc., supporting the functions of adding, modifying, disabling and viewing personnel, ensuring the integrity and accuracy of personnel information, and facilitating subsequent salary calculation and management;

[0065] Basic salary management: used to manage employees' basic salary information, including basic salary standards, salary calculation structure and adjustment records;

[0066] Commission management: used to define commission plans linked to employee performance, and supports flexible setting of commission calculation rules;

[0067] Subsidy management: used to manage various subsidy items, such as transportation subsidies, catering subsidies and communication subsidies;

[0068] Assessment deduction management: record and manage deductions due to failure to meet assessment standards;

[0069] Special withholding management: handling withholdings in specific circumstances, including union dues and insurance;

[0070] Standard element item management: used to maintain standard element items in the system, including name, description and calculation rules;

[0071] Affiliate company element management: Efficiently manage the customized element items of the affiliate companies and support the association with standard element items;

[0072] Element mapping relationship management: used to define the mapping relationship between the elements of the franchised company and the standard elements, so as to achieve standardized management of salary elements among companies;

[0073] Payroll scheme management: Design and manage a variety of payroll schemes, support flexible configuration and maintenance of models, and enable finance staff to quickly adjust salary calculation schemes according to different situations;

[0074] Payroll management: Provides payroll viewing, adjustment and confirmation functions, allowing financial personnel to fully manage each employee's payroll.

[0075] By adopting the above technical solution, automatic generation of salary data is achieved.

[0076] Compared with the prior art, the present invention has the following beneficial effects: the function uses advanced natural language processing technology to support intelligent analysis of various salary-related terms and descriptions. For example, when faced with expressions such as "basic salary" or "fixed salary", the system can automatically and accurately map them to the "basic salary" element item. At the same time, for descriptions related to employee clothing, such as "not wearing work clothes" or "unqualified work clothes", the system can also reasonably identify and automatically associate them with the "work clothes" item. Through a deep understanding of semantics, the accuracy and consistency of salary calculation are ensured;

[0077] The system not only has the initial intelligent matching capability, but also can form self-optimization and dynamic adjustment by learning the mapping relationship between franchise companies in the entire network. The system will regularly analyze data from different franchise companies and identify the mapping mode of element items that conform to the actual situation, so as to continuously improve the accuracy of matching. This learning mechanism enables the system to always maintain efficient adaptability and flexibility in the ever-changing market environment;

[0078] A highlight of the system is that it supports many-to-one mapping relationships. For example, when multiple assessment indicators need to be merged, "24-hour sign-off rate assessment" and "22-hour sign-off rate assessment" can be uniformly mapped to the element "sign-off rate assessment". This flexible mapping capability enables companies to consider various assessment factors more comprehensively and enhance the rationality and incentives of remuneration.

[0079] In addition to the powerful background intelligent matching function, the system also provides an intuitive and easy-to-use user interface, allowing users to easily view, manage and adjust the mapping relationship between standard elements and franchised company elements. Through clear visual display and interactive functions, users can quickly understand the mapping logic and results, effectively reducing the complexity of operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 is a flow chart of the steps of the present invention;

[0081] Figure 2 This is a system framework diagram of the present invention. DETAILED DESCRIPTION

[0082] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0083] See also Figure 1-2 The present invention provides a technical solution: a method for automatically generating salary data for the express delivery industry, characterized in that the method for automatically generating salary data includes: creating a salary model, naming the salary model and selecting applicable personnel; uploading the historical salary slip files of the selected applicable personnel to the system to obtain relevant salary data; parsing the salary slip files of historical months to obtain franchise company elements; establishing a mapping relationship between franchise company elements and standard elements through an intelligent matching algorithm; presenting the generated mapping relationship to the franchise company's financial personnel, and the franchise company's financial personnel adjust and confirm the matching rules.

[0084] The creation of a salary model, naming the salary model and selecting applicable personnel specifically include:

[0085] The user logs in to the salary management system using an account, enters the salary model management module, creates a new salary model, and enters basic information of the salary model, including the model name and description;

[0086] On the page for creating a new model, fill in the model name, select applicable personnel, and select all employees in a specific department. After completing the selection, the user needs to view the list of selected personnel, verify, and save the selected personnel;

[0087] After completing the naming and selecting the applicable personnel, the user clicks the "Save Model" button, and the system automatically saves the salary model and related personnel information to the database;

[0088] After saving, return to the salary model management page and upload the historical payroll file.

[0089] The above-mentioned uploading the historical payroll file of the selected applicable personnel to the system and obtaining the relevant salary data specifically includes:

[0090] The user logs in to the salary management system with his / her own account, finds and clicks the "Historical Payroll Upload" module in the main interface to enter the upload interface;

[0091] On the upload page, users need to select the salary model they created previously to ensure that the uploaded file matches the model;

[0092] Select the appropriate payroll model from the drop-down menu, find "Upload File", click the button to open the file selection dialog box, in the file selection dialog box, browse your computer to find and select the historical payroll file containing the selected applicable personnel;

[0093] After selecting the file, the file name is displayed in the corresponding position on the upload page, and the user confirms that the selected file is correct;

[0094] Click the "Upload" button, the system starts uploading the file to the server, the system displays the upload progress bar, and obtains the relevant salary data. After the file is uploaded, the system automatically parses the file and extracts the relevant salary data;

[0095] After the upload is complete, the system will display the parsing results, including successfully extracted data items, error messages, and data integrity checks;

[0096] The user needs to check whether the data is accurate and ensure that there are no omissions or errors. After checking the parsing results, the user clicks the "Confirm Data" button to save the extracted salary data into the system;

[0097] If an error is found, the user selects "Re-upload" and repeats the steps. After confirming the data, the user chooses to return to the upload module.

[0098] The parsing of the payroll file of the historical month to obtain the elements of the franchised company specifically includes:

[0099] Open the payroll file, check the column headers of the file, identify the columns containing the "franchise company" information, and get the layout and format of the data;

[0100] Check data integrity to ensure there are no missing values ​​and format errors, clean the data, and remove unnecessary rows and columns;

[0101] According to the column headers of the file, find the column containing the information of the franchised company, copy the data of the relevant column, and use the data filtering function to extract the required information;

[0102] Paste the extracted franchise company information into a new table, organize it, remove duplicates, sort and classify the data;

[0103] Count the number of people and total wages of each company, and use pivot tables and chart tools to display the analysis results;

[0104] Save the sorted franchise company data as a new file, write a data analysis report, and summarize the analysis results.

[0105] The establishment of a mapping relationship between the franchise company element items and the standard element items through the intelligent matching algorithm specifically includes:

[0106] Collect data on franchised companies, including company name and address, and collect data on standard elements, including standard name and classification;

[0107] Ensure data format is consistent and remove duplicates and missing items;

[0108] Remove redundant characters and spaces, handle uppercase and lowercase letters, standardize names, and perform word segmentation on text data;

[0109] Extract feature vectors for each element and convert text into numerical vectors using TF-IDF;

[0110] Calculate the similarity between the elements of the franchised company and the standard elements;

[0111] Set a similarity threshold, select matching items above the threshold as valid mappings, record the elements of the franchised company and the corresponding standard elements, use the cross-validation method to verify the results, ensure the accuracy of the matching results, and recalculate inaccurate mapping items;

[0112] Regularly update standard element items and franchised company element item data, re-run the matching algorithm, and ensure the timeliness of the mapping relationship.

[0113] The calculation of the similarity between the franchised company element item and the standard element item specifically includes:

[0114] Among them, the cosine similarity calculation formula is:

[0115]

[0116] In the formula, A and B represent two vectors, n represents the dimension of the vector, and A i represents the value of the i-th element in vector A, B i represents the value of the i-th element in vector B, represents the dot product of vectors A and B, represents the square of the modulus of vector A, Represents the square of the magnitude of vector B.

[0117] The generated mapping relationship is presented to the financial personnel of the franchised company, and the financial personnel of the franchised company adjust and confirm the matching rules, which specifically include:

[0118] Organize and generate detailed reports containing mapping relationships, clearly presenting data and similarity results;

[0119] Organize a meeting and invite financial personnel and related teams to introduce the background and significance of the mapping relationship, display the mapping relationships one by one, and explain the basis and calculation method of each mapping;

[0120] Seek opinions and suggestions from finance staff, identify inaccurate mapping items that need to be adjusted, and adjust the mapping rules based on the feedback. Finance staff confirm the adjusted mapping relationship and conduct secondary verification to ensure accuracy;

[0121] Document the final confirmed mapping relationship and adjustment rules, set up a follow-up tracking plan, and regularly review the effectiveness and accuracy of the mapping relationship.

[0122] A system for automatically generating salary data for the express delivery industry, the system for automatically generating salary data comprising:

[0123] Personnel management module: used to manage the basic information of all personnel in the company;

[0124] Basic salary management module: The basic salary management module is electrically connected to the personnel management module, and the basic salary management module is used to manage the basic salary information of employees;

[0125] Commission management module: The commission management module is electrically connected to the basic salary management module, and the commission management module is used to define a commission plan linked to employee performance;

[0126] Subsidy management module: The subsidy management module is electrically connected to the commission management module, and the subsidy management module is used to manage various subsidy projects;

[0127] Assessment deduction management module: the assessment deduction management module is electrically connected to the subsidy management module, and the assessment deduction management module is used to record the deductions caused by failure to meet the assessment standards;

[0128] Standard element item management module: The standard element item management module is electrically connected to the assessment deduction management module, and the standard element item management module is used to maintain the information of the standard element items.

[0129] The application layer specifically includes:

[0130] Data synchronization module: realizes data synchronization between various functional modules to ensure real-time sharing and updating of information;

[0131] Data cleaning module: cleans the original data to remove errors, duplications and invalid data;

[0132] Machine learning module: Apply machine learning algorithms to analyze historical salary data and provide intelligent decision support and salary prediction models;

[0133] Rule engine module: flexibly defines and manages various rules for salary calculation, supports dynamic adjustment, and allows financial personnel to handle complex salary calculations without modifying the underlying code, greatly improving work efficiency;

[0134] Payroll generation and refresh module: automatically generates and refreshes payroll according to the set payroll scheme and calculation rules to ensure timely update and accurate generation;

[0135] Log module: comprehensively record system operation logs and track key operations in the payroll processing process.

[0136] The middle layer specifically includes:

[0137] Personnel management: responsible for managing the basic information of all personnel in the company, including personal information, position, department, date of employment, etc., supporting the functions of adding, modifying, disabling and viewing personnel, ensuring the integrity and accuracy of personnel information, and facilitating subsequent salary calculation and management;

[0138] Basic salary management: used to manage employees' basic salary information, including basic salary standards, salary calculation structure and adjustment records;

[0139] Commission management: used to define commission plans linked to employee performance, and supports flexible setting of commission calculation rules;

[0140] Subsidy management: used to manage various subsidy items, such as transportation subsidies, catering subsidies and communication subsidies;

[0141] Assessment deduction management: record and manage deductions due to failure to meet assessment standards;

[0142] Special withholding management: handling withholdings in specific circumstances, including union dues and insurance;

[0143] Standard element item management: used to maintain standard element items in the system, including name, description and calculation rules;

[0144] Affiliate company element management: Efficiently manage the customized element items of the affiliate companies and support the association with standard element items;

[0145] Element mapping relationship management: used to define the mapping relationship between the elements of the franchised company and the standard elements, so as to achieve standardized management of salary elements among companies;

[0146] Payroll scheme management: Design and manage a variety of payroll schemes, support flexible configuration and maintenance of models, and enable finance staff to quickly adjust salary calculation schemes according to different situations;

[0147] Payroll management: Provides payroll viewing, adjustment and confirmation functions, allowing financial personnel to fully manage each employee's payroll.

Claims

1. A method for automatically generating salary data for the express delivery industry, characterized in that: Methods for automatically generating salary data include: Create a salary model, name the salary model and select applicable personnel; Upload the historical payroll files of the selected applicable personnel into the system to obtain relevant salary data; Parse the payroll files of historical months and obtain the elements of the affiliated companies; Through intelligent matching algorithms, a mapping relationship between franchised company elements and standard elements is established; The generated mapping relationship is presented to the financial personnel of the franchised company, who adjust and confirm the matching rules.

2. A method for automatically generating salary data for the express delivery industry according to claim 1, characterized in that: The creation of a salary model, naming the salary model and selecting applicable personnel specifically include: The user logs in to the salary management system using an account, enters the salary model management module, creates a new salary model, and enters basic information of the salary model, including the model name and description; On the page for creating a new model, fill in the model name, select applicable personnel, and select all employees in a specific department. After completing the selection, the user needs to view the list of selected personnel, verify, and save the selected personnel; After completing the naming and selecting the applicable personnel, the user clicks the "Save Model" button, and the system automatically saves the salary model and related personnel information to the database; After saving, return to the salary model management page and upload the historical payroll file.

3. The method for automatically generating salary data for the express delivery industry according to claim 2, characterized in that: The above-mentioned uploading the historical payroll file of the selected applicable personnel to the system to obtain the relevant salary data specifically includes: The user logs in to the salary management system with his / her own account, finds and clicks the "Historical Payroll Upload" module in the main interface to enter the upload interface; On the upload page, users need to select the salary model they created previously to ensure that the uploaded file matches the model; Select the appropriate payroll model from the drop-down menu, find "Upload File", and click the button to open the file selection dialog box. In the file selection dialog box, browse your computer to find and select the historical payroll file containing the selected applicable personnel; After selecting the file, the file name is displayed in the corresponding position on the upload page, and the user confirms that the selected file is correct; Click the "Upload" button, the system starts uploading the file to the server, the system displays the upload progress bar, and obtains the relevant salary data. After the file is uploaded, the system automatically parses the file and extracts the relevant salary data; After the upload is complete, the system will display the parsing results, including successfully extracted data items, error messages, and data integrity checks; The user needs to check whether the data is accurate and ensure that there are no omissions or errors. After checking the parsing results, the user clicks the "Confirm Data" button to save the extracted salary data into the system; If an error is found, the user selects "Re-upload" and repeats the steps. After confirming the data, the user chooses to return to the upload module.

4. A method for automatically generating salary data for the express delivery industry according to claim 3, characterized in that: The parsing of the payroll file of the historical month to obtain the elements of the affiliated company specifically includes: Open the payroll file, check the column headers of the file, identify the columns containing the "franchise company" related information, and get the layout and format of the data; Check data integrity to ensure there are no missing values ​​and format errors, clean the data, and remove unnecessary rows and columns; According to the column headers of the file, find the column containing the information of the franchised company, copy the data of the relevant column, and use the data filtering function to extract the required information; Paste the extracted franchise company information into a new table, organize it, remove duplicates, sort and classify the data; Count the number of people and total wages of each company, and use pivot tables and chart tools to display the analysis results; Save the sorted franchise company data as a new file, write a data analysis report, and summarize the analysis results.

5. A method for automatically generating salary data for the express delivery industry according to claim 4, characterized in that: The establishment of a mapping relationship between the franchise company element items and the standard element items through the intelligent matching algorithm specifically includes: Collect data on franchised companies, including company name and address, and collect data on standard elements, including standard name and classification; Ensure data format is consistent and remove duplicates and missing items; Remove redundant characters and spaces, handle uppercase and lowercase letters, standardize names, and perform word segmentation on text data; Extract feature vectors for each element and convert text into numerical vectors using TF-IDF; Calculate the similarity between the elements of the franchised company and the standard elements; Set a similarity threshold, select matching items above the threshold as valid mappings, record the elements of the franchised company and the corresponding standard elements, use the cross-validation method to verify the results, ensure the accuracy of the matching results, and recalculate inaccurate mapping items; Regularly update standard element items and franchised company element item data, re-run the matching algorithm, and ensure the timeliness of the mapping relationship.

6. A method for automatically generating salary data for the express delivery industry according to claim 5, characterized in that: The calculation of the similarity between the franchised company element item and the standard element item is as follows: include: Among them, the cosine similarity calculation formula is: In the formula, A and B represent two vectors, n represents the dimension of the vector, and A i represents the value of the i-th element in vector A, B i represents the value of the i-th element in vector B, represents the dot product of vectors A and B, represents the square of the modulus of vector A, Represents the square of the magnitude of vector B.

7. A method for automatically generating salary data for the express delivery industry according to claim 6, characterized in that: The generated mapping relationship is presented to the financial personnel of the franchised company, and the financial personnel of the franchised company adjust and confirm the matching rules, which specifically include: Organize and generate detailed reports containing mapping relationships, clearly presenting data and similarity results; Organize a meeting and invite financial personnel and related teams to introduce the background and significance of the mapping relationship, display the mapping relationships one by one, and explain the basis and calculation method of each mapping; Seek opinions and suggestions from finance staff, identify inaccurate mapping items that need to be adjusted, and adjust the mapping rules based on the feedback. Finance staff confirm the adjusted mapping relationship and conduct secondary verification to ensure accuracy; Document the final confirmed mapping relationship and adjustment rules, set up a follow-up tracking plan, and regularly review the effectiveness and accuracy of the mapping relationship.

8. A system for automatically generating salary data for the express delivery industry, characterized in that: The system for automatically generating salary data includes: Personnel management module: used to manage the basic information of all personnel in the company; Basic salary management module: The basic salary management module is electrically connected to the personnel management module, and the basic salary management module is used to manage the basic salary information of employees; Commission management module: The commission management module is electrically connected to the basic salary management module, and the commission management module is used to define a commission plan linked to employee performance; Subsidy management module: The subsidy management module is electrically connected to the commission management module, and the subsidy management module is used to manage various subsidy projects; Assessment deduction management module: the assessment deduction management module is electrically connected to the subsidy management module, and the assessment deduction management module is used to record the deductions caused by failure to meet the assessment standards; Standard element item management module: The standard element item management module is electrically connected to the assessment deduction management module, and the standard element item management module is used to maintain the information of the standard element items.

9. A system for automatically generating salary data for the express delivery industry according to claim 8, characterized in that: The application layer specifically includes: Data synchronization module: realizes data synchronization between various functional modules to ensure real-time sharing and updating of information; Data cleaning module: cleans the original data to remove errors, duplications and invalid data; Machine learning module: Apply machine learning algorithms to analyze historical salary data and provide intelligent decision support and salary prediction models; Rule engine module: flexibly defines and manages various rules for salary calculation, supports dynamic adjustment, and allows financial personnel to handle complex salary calculations without modifying the underlying code, greatly improving work efficiency; Payroll generation and refresh module: automatically generates and refreshes payrolls according to the set payroll scheme and calculation rules to ensure timely updates and accurate generation; Log module: comprehensively record system operation logs and track key operations in the payroll processing process.

10. A system for automatically generating salary data for the express delivery industry according to claim 9, characterized in that: The middle layer specifically includes: Personnel management: responsible for managing the basic information of all personnel in the company, including personal information, position, department, date of employment, etc., supporting the functions of adding, modifying, disabling and viewing personnel, ensuring the integrity and accuracy of personnel information, and facilitating subsequent salary calculation and management; Basic salary management: used to manage employees' basic salary information, including basic salary standards, salary calculation structure and adjustment records; Commission management: used to define commission plans linked to employee performance, and supports flexible setting of commission calculation rules; Subsidy management: used to manage various subsidy items, such as transportation subsidies, catering subsidies and communication subsidies; Assessment deduction management: record and manage deductions due to failure to meet assessment standards; Special withholding management: handling withholdings in specific circumstances, including union dues and insurance; Standard element item management: used to maintain standard element items in the system, including name, description and calculation rules; Affiliate company element management: Efficiently manage customized element items of affiliate companies and support association with standard element items; Element mapping relationship management: used to define the mapping relationship between the elements of the franchised company and the standard elements, so as to achieve standardized management of salary elements among companies; Payroll scheme management: Design and manage a variety of payroll schemes, support flexible configuration and maintenance of models, and enable finance staff to quickly adjust salary calculation schemes according to different situations; Payroll management: Provides payroll viewing, adjustment and confirmation functions, allowing financial personnel to fully manage each employee's payroll.

Images